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<div class="elementToProof" style="color: rgb(200, 38, 19);"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 13pt;"><b>CORRECTION:
</b></span><span style="font-family: Arial, sans-serif; font-size: 13pt;"><b>3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street,
</b></span><span style="font-family: Arial, sans-serif; font-size: 14pt;"><b><u>11th floor</u></b></span><span style="font-family: Arial, sans-serif; font-size: 13pt;"><b><u>,</u> there will be light snacks and beverages in the
<u>kitchen area, Rm. 1105C</u>.</b></span></div>
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<b><a href="https://statistics.yale.edu/" id="OWA34a87e37-3802-c433-1e64-bfb8f99b6910" class="x_x_OWAAutoLink" title="Home" data-auth="NotApplicable" style="color: rgb(40, 109, 192); margin-top: 0px; margin-bottom: 0px;">Department of Statistics and Data Science</a></b></div>
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<p style="margin-top: 0px; margin-bottom: 0px;"><span style="font-family: Arial, sans-serif; font-size: 12pt;"><b> </b></span></p>
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<span style="line-height: normal;"><b>Thuy-Duong "June" Vuong</b></span><b>, Miller Institute, Berkeley</b></div>
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Date: Monday, March 24, 2025</div>
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Time: 4:00PM to 5:00PM</div>
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Location: Kline Tower, 13th Floor, Rm. 1327 <span style="color: rgb(40, 109, 192);">
<u><a href="http://maps.google.com/?q=219+Prospect+Street%2C+New+Haven%2C+CT%2C+06511%2C+us" id="OWA18aff70c-f5d6-aaf5-2338-467692c3bf07" class="x_x_OWAAutoLink" data-auth="NotApplicable" style="color: rgb(40, 109, 192);">See map</a></u></span> </div>
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219 Prospect Street</div>
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New Haven, CT 06511</div>
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Webcast Option: <a href="https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ea4e0f03-5e28-48a7-b343-b233012bce08" id="OWA11d3ea0a-9b9e-634d-0b1d-282a6e4446f6" class="x_OWAAutoLink" data-auth="NotApplicable">
https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ea4e0f03-5e28-48a7-b343-b233012bce08</a></div>
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<b>Title: Efficiently learning and sampling from multimodal distributions using data-based initialization</b></div>
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<b>Information and Abstract:</b> Learning to sample is a central task in generative AI: the goal is to generate (infinitely many more) samples from a target distribution $\mu$ given a small number of samples from $\mu.$ It is well-known that traditional algorithms
such as Glauber or Langevin dynamics are highly inefficient when the target distribution is multimodal, as they take exponential time to converge from a \emph{worst case start}, while recently proposed algorithms such as denoising diffusion (DDPM) require
information that is computationally hard to learn. In this talk, we propose a novel and conceptually simple algorithmic framework to learn multimodal target distributions by initializing traditional sampling algorithms at the empirical distribution. As applications,
we show new results for two representative distribution families: Gaussian mixtures and Ising models. When the target distribution $\mu$ is a mixture of $k$ well-conditioned Gaussians, we show that the (continuous) Langevin dynamics initialized from the empirical
distribution over $\tilde{O}(k/\epsilon^2)$ samples, with high probability over the samples, converge to $\mu” in $\tilde{O}(1)$-time; both the number of samples and convergence time are optimal. When $\mu$ is a low-complexity Ising model, we show a similar
result for the Glauber dynamics with approximate marginals learned via pseudolikelihood estimation, demonstrating for the first time that such low-complexity Ising models can be efficiently learned from samples.”</div>
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Based on joint work with Frederic Koehler and Holden Lee.</div>
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3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13 floor, there will be light snacks and beverages in the kitchen area.</div>
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<p style="margin-top: 0px; margin-bottom: 0px;"><span style="font-family: Arial, sans-serif; color: black;">For more details and upcoming events visit our website at
</span><span style="font-family: Arial, sans-serif; color: rgb(70, 120, 134);"><a href="https://statistics.yale.edu/calendar" id="OWA6ad2b01b-ef30-ab6f-15ae-07db1149d507" class="x_x_OWAAutoLink" data-auth="NotApplicable" style="color: rgb(70, 120, 134); margin-top: 0px; margin-bottom: 0px;">https://statistics.yale.edu/calendar</a></span><span style="font-family: Arial, sans-serif;">.</span></p>
<p style="margin-top: 0px; margin-bottom: 0px;"><span style="font-family: Arial, sans-serif; font-size: 11pt;"> </span></p>
<p style="margin-top: 0px; margin-bottom: 0px;"><span style="font-family: Arial, sans-serif; font-size: 18pt;">Department of Statistics and Data Science</span></p>
<p style="margin-top: 0px; margin-bottom: 0px;"><span style="font-family: Arial, sans-serif; font-size: 9pt; color: black;">Yale University<br>
Kline Tower</span></p>
<p style="margin-top: 0px; margin-bottom: 0px;"><span style="font-family: Arial, sans-serif; font-size: 9pt; color: black;">219 Prospect Street<br>
New Haven, CT 06511</span></p>
<p style="margin-top: 0px; margin-bottom: 0px;"><span style="font-size: 11pt; color: rgb(70, 120, 134);"><a href="https://statistics.yale.edu/" id="OWAf39d8813-c6fd-3482-e058-e5cfd8f44675" class="x_x_OWAAutoLink" data-auth="NotApplicable" style="color: rgb(70, 120, 134); margin-top: 0px; margin-bottom: 0px;">https://statistics.yale.edu/</a></span></p>
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